However, the characteristics of raw camera's sensor data (e.g. linear relationship with scene irradiance at each in 12-14 bits, unprocessed signal and noise samples) are often better suited for the inverse problems that commonly arise in low-level vision tasks such as denoising, deblurring, super-resolution [1]}, [2]}, [3]}, [4]}. Professional photographers also commonly choose to process RAW images by themselves to produce images with better visual effects [5]}.
Unfortunately, RAW image datasets are not as common and diverse as their RGB counterparts, therefore, CNN-based approaches might not reach their full potential. To bridge this gap, we introduce the AIM Reversed ISP Challenge and review current solutions.
We can find metadata-based raw reconstruction methods for de-render or unprocess the RGB image back to its original raw values [6]}, [7]}, [8]}, these methods usually require specific metadata stored as an overhead (e.g.  64 KB).
Similarly, UPI [9]} proposes a generic camera ISP model composed of five canonical and invertible steps [10]}, [5]}. This simple approach can produce realistic raw sensor data for training denoising models [9]}. However, it requires specific camera parameters (e.g. correction matrices, digital gains) that are generally inaccessible.
